Application of Machine Learning for Sensors Network Resource Management
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 9466
Special Issue Editors
Interests: network security; cloud computing; security; computer networking; network communication; networking; information and communication technology; information technology; IT security; PHP
Special Issue Information
Dear Colleagues,
Machine learning techniques can be applied to solve various issues in computer networks. Recently, researchers have advanced the study of Network Intrusion Detection, Network Traffic Optimization, Fault Detection, Network Resource Management, and QoS Management utilizing various ML techniques ranging from reinforcement learning to federated learning. As an example, machine learning algorithms can be used to analyze network traffic and detect anomalies or suspicious activities that may indicate an intrusion or malicious behavior. By training models on labeled datasets of normal and attack traffic, machine learning can help identify patterns and classify network traffic in real-time. Similarly, machine learning can be applied to optimize network traffic and improve network performance. By analyzing historical data, machine learning algorithms can identify patterns in network traffic, predict network congestion, and optimize routing protocols to ensure efficient data transmission. Therefore, the editors seek original submissions on the following topics: FL for network resource management; ML for network traffic and content analysis; resource management using ML for fog, edge and cloud computing; network traffic prediction for resource allocation; QoS and energy management of network resources; identifying anomalous traffic patterns in IoT; and enabling blockchain-based applications for large-scale networks.
Dr. Junaid Shuja
Dr. Atta ur Rehman Khan
Guest Editors
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Keywords
- machine learning
- blockchain
- federated learning
- network resource management
- QoS
- edge computing
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